{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAd865lNzZpT"
   },
   "source": [
    "#  <span style=\"color:orange\">Regression Tutorial (REG101) - Level Beginner</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXN8UznszZpc"
   },
   "source": [
    "**Created using: PyCaret 2.0** <br />\n",
    "**Date Updated: August 24, 2020**\n",
    "\n",
    "# 1.0  Tutorial Objective\n",
    "Welcome to Regression Tutorial (REG101) - Level Beginner. This tutorial assumes that you are new to PyCaret and looking to get started with Regression using the `pycaret.regression` Module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "\n",
    "* **Getting Data:**  How to import data from PyCaret repository\n",
    "* **Setting up Environment:**  How to setup an experiment in PyCaret and get started with building regression models\n",
    "* **Create Model:**  How to create a model, perform cross validation and evaluate regression metrics\n",
    "* **Tune Model:**  How to automatically tune the hyperparameters of a regression model\n",
    "* **Plot Model:**  How to analyze model performance using various plots\n",
    "* **Finalize Model:** How to finalize the best model at the end of the experiment\n",
    "* **Predict Model:**  How to make prediction on new / unseen data\n",
    "* **Save / Load Model:**  How to save / load a model for future use\n",
    "\n",
    "Read Time : Approx. 30 Minutes\n",
    "\n",
    "\n",
    "## 1.1 Installing PyCaret\n",
    "The first step to get started with PyCaret is to install PyCaret. Installation is easy and will only take a few minutes. Follow the instructions below:\n",
    "\n",
    "#### Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install pycaret`  <br />\n",
    "\n",
    "#### Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install pycaret`\n",
    "\n",
    "\n",
    "## 1.2 Pre-Requisites\n",
    "- Python 3.6 or greater\n",
    "- PyCaret 2.0 or greater\n",
    "- Internet connection to load data from pycaret's repository\n",
    "- Basic Knowledge of Regression\n",
    "\n",
    "## 1.3 For Google Colab Users:\n",
    "If you are running this notebook on Google colab, run the following code at top of your notebook to display interactive visuals.<br/>\n",
    "<br/>\n",
    "`from pycaret.utils import enable_colab` <br/>\n",
    "`enable_colab()`\n",
    "\n",
    "## 1.4 See also:\n",
    "- __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__\n",
    "- __[Regression Tutorial (REG103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Expert%20-%20REG103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HuEUiXXhzZpi"
   },
   "source": [
    "# 2.0 What is Regression?\n",
    "\n",
    "Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable', or 'target') and one or more independent variables (often called 'features', 'predictors', or 'covariates'). The objective of regression in machine learning is to predict continuous values such as sales amount, quantity, temperature etc.\n",
    "\n",
    "__[Learn More about Regression](https://hbr.org/2015/11/a-refresher-on-regression-analysis)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xnEk7n5ZzZpm"
   },
   "source": [
    "# 3.0 Overview of the Regression Module in PyCaret\n",
    "PyCaret's Regression module (`pycaret.regression`) is a supervised machine learning module which is used for predicting continuous values / outcomes using various techniques and algorithms. Regression can be used for predicting values / outcomes such as sales, units sold, temperature or any number which is continuous.\n",
    "\n",
    "PyCaret's regression module has over 25 algorithms and 10 plots to analyze the performance of models. Be it hyper-parameter tuning, ensembling or advanced techniques like stacking, PyCaret's regression module has it all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uN95Uqo6zZpq"
   },
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Guj8GFIJzZpu"
   },
   "source": [
    "For this tutorial we will use a dataset based on a case study called **\"Sarah Gets a Diamond\"**. This case was presented in the first year decision analysis course at Darden School of Business (University of Virginia). The basis for the data is a case regarding a hopeless romantic MBA student choosing the right diamond for his bride-to-be, Sarah. The data contains 6000 records for training. Short descriptions of each column are as follows:\n",
    "\n",
    "- **ID:** Uniquely identifies each observation (diamond)\n",
    "- **Carat Weight:** The weight of the diamond in metric carats. One carat is equal to 0.2 grams, roughly the same weight as a paperclip\n",
    "- **Cut:** One of five values indicating the cut of the diamond in the following order of desirability (Signature-Ideal, Ideal, Very Good, Good, Fair)\n",
    "- **Color:** One of six values indicating the diamond's color in the following order of desirability (D, E, F - Colorless, G, H, I - Near colorless)\n",
    "- **Clarity:** One of seven values indicating the diamond's clarity in the following order of desirability (F - Flawless, IF - Internally Flawless, VVS1 or VVS2 - Very, Very Slightly Included, or VS1 or VS2 - Very Slightly Included, SI1 - Slightly Included)\n",
    "- **Polish:** One of four values indicating the diamond's polish (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Symmetry:** One of four values indicating the diamond's symmetry (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Report:** One of of two values \"AGSL\" or \"GIA\" indicating which grading agency reported the qualities of the diamond qualities\n",
    "- **Price:** The amount in USD that the diamond is valued `Target Column`\n",
    "\n",
    "\n",
    "#### Dataset Acknowledgement:\n",
    "This case was prepared by Greg Mills (MBA ’07) under the supervision of Phillip E. Pfeifer, Alumni Research Professor of Business Administration. Copyright (c) 2007 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved.\n",
    "\n",
    "The original dataset and description can be __[found here.](https://github.com/DardenDSC/sarah-gets-a-diamond)__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wwUzzm1YzZpz"
   },
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFCSZ_NKzZp3"
   },
   "source": [
    "You can download the data from the original source __[found here](https://github.com/DardenDSC/sarah-gets-a-diamond)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using the `get_data()` function (This will require internet connection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:42.891205Z",
     "start_time": "2020-04-23T13:12:42.168861Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "H6qS5U--zZp7",
    "outputId": "2a11a81c-7e67-425a-a3ef-091d2c9fbd30"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight    Cut Color Clarity Polish Symmetry Report  Price\n",
       "0          1.10  Ideal     H     SI1     VG       EX    GIA   5169\n",
       "1          0.83  Ideal     H     VS1     ID       ID   AGSL   3470\n",
       "2          0.85  Ideal     H     SI1     EX       EX    GIA   3183\n",
       "3          0.91  Ideal     E     SI1     VG       VG    GIA   4370\n",
       "4          0.83  Ideal     G     SI1     EX       EX    GIA   3171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('diamond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:45.180048Z",
     "start_time": "2020-04-23T13:12:45.176712Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "D5PerU66zZqK",
    "outputId": "2fdd6ab8-7d68-4cc4-81a7-0cb82ed70799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of data\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eWmeLvYzZqY"
   },
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, a sample of 600 records has been withheld from the original dataset to be used for predictions. This should not be confused with a train/test split as this particular split is performed to simulate a real life scenario. Another way to think about this is that these 600 records are not available at the time when the machine learning experiment was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:13:11.922396Z",
     "start_time": "2020-04-23T13:13:11.912627Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "R4K9F7BXzZqc",
    "outputId": "22b1c4e7-a1e1-48d2-8ddc-907e716d5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (5400, 8)\n",
      "Unseen Data For Predictions: (600, 8)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.9, random_state=786)\n",
    "data_unseen = dataset.drop(data.index)\n",
    "\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data_unseen.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxnJV14BzZqq"
   },
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15-blMPOzZqw"
   },
   "source": [
    "The `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes two mandatory parameters: a pandas dataframe and the name of the target column. All other parameters are optional and are used to customize the pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To account for this, PyCaret displays a table containing the features and their inferred data types after `setup()` is executed. If all of the data types are correctly identified `enter` can be pressed to continue or `quit` can be typed to end the expriment. Ensuring that the data types are correct is of fundamental importance in PyCaret as it automatically performs a few pre-processing tasks which are imperative to any machine learning experiment. These tasks are performed differently for each data type which means it is very important for them to be correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's inferred data type using the `numeric_features` and `categorical_features` parameters in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:16:02.144917Z",
     "start_time": "2020-04-23T13:15:57.427822Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "id": "7V2FN4KQzZrA",
    "outputId": "43d8d23d-ef08-438a-8cc3-ba78e9773aca",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow3_col1\" class=\"data row3 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow5_col1\" class=\"data row5 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow6_col1\" class=\"data row6 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow10_col1\" class=\"data row10 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow11_col1\" class=\"data row11 col1\" >(3779, 29)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow12_col1\" class=\"data row12 col1\" >(1621, 29)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83dalevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_0029e4a8_e630_11ea_bcd5_482ae32b83darow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b55a332c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "exp_reg101 = setup(data = data, target = 'Price', session_id=123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWBypX32zZrP"
   },
   "source": [
    "Once the setup has been succesfully executed it prints the information grid which contains several important pieces of information. Most of the information is related to the pre-processing pipeline which is constructed when `setup()` is executed. The majority of these features are out of scope for the purposes of this tutorial. However, a few important things to note at this stage include:\n",
    "\n",
    "- **session_id :**  A pseudo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the `session_id` is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (5400, 8) means 5400 samples and 8 features including the target column. <br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in the original data, this will show as True. For this experiment there are no missing values in the dataset.<br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 1 out of 8 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 6 out of 8 features are inferred as categorical. <br/>\n",
    "<br/>\n",
    "- **Transformed Train Set :** Displays the shape of the transformed training set. Notice that the original shape of (5400, 8) is transformed into (3779, 28) for the transformed train set. The number of features has increased from 8 from 28 due to categorical encoding <br/>\n",
    "<br/>\n",
    "- **Transformed Test Set :** Displays the shape of transformed test/hold-out set. There are 1621 samples in test/hold-out set. This split is based on the default value of 70/30 that can be changed using `train_size` parameter in setup. <br/>\n",
    "\n",
    "Notice how a few tasks that are imperative to perform modeling are automatically handled, such as missing value imputation (in this case there are no missing values in training data, but we still need imputers for unseen data), categorical encoding etc. Most of the parameters in `setup()` are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to the intermediate and expert levels, we will cover them in much greater detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xBqHzabEzZrT"
   },
   "source": [
    "# 7.0 Comparing All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHiNl6UmzZrW"
   },
   "source": [
    "Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using k-fold cross validation for metric evaluation. The output prints a score grid that shows average MAE, MSE, RMSE, R2, RMSLE and MAPE accross the folds (10 by default) along with training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:21:22.884291Z",
     "start_time": "2020-04-23T13:20:11.108031Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "atJfMGD6zZrb",
    "outputId": "af936da0-cc93-4429-ef61-20d940e0aa4e",
    "scrolled": false
   },
   "outputs": [
    {
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            background-color:  lightgrey;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col4 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
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       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "        }</style><table id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col1\" class=\"data row0 col1\" >627.9862</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col2\" class=\"data row0 col2\" >2177794.5416</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col3\" class=\"data row0 col3\" >1395.8072</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.9795</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0677</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col6\" class=\"data row0 col6\" >0.0496</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow0_col7\" class=\"data row0 col7\" >2.1437</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col1\" class=\"data row1 col1\" >755.9566</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col2\" class=\"data row1 col2\" >2756642.5198</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col3\" class=\"data row1 col3\" >1604.2591</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.9732</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0819</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col6\" class=\"data row1 col6\" >0.0607</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow1_col7\" class=\"data row1 col7\" >0.3697</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col1\" class=\"data row2 col1\" >752.2357</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col2\" class=\"data row2 col2\" >3055569.4004</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col3\" class=\"data row2 col3\" >1687.7770</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.9711</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0773</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col6\" class=\"data row2 col6\" >0.0567</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow2_col7\" class=\"data row2 col7\" >0.1131</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col0\" class=\"data row3 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col1\" class=\"data row3 col1\" >762.0579</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col2\" class=\"data row3 col2\" >2969421.3714</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col3\" class=\"data row3 col3\" >1671.9701</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.9711</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0817</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col6\" class=\"data row3 col6\" >0.0597</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow3_col7\" class=\"data row3 col7\" >0.3608</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col1\" class=\"data row4 col1\" >932.7974</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col2\" class=\"data row4 col2\" >3534049.4485</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col3\" class=\"data row4 col3\" >1855.3299</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.9652</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.1061</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col6\" class=\"data row4 col6\" >0.0801</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow4_col7\" class=\"data row4 col7\" >0.1544</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col1\" class=\"data row5 col1\" >919.7893</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col2\" class=\"data row5 col2\" >3751333.9600</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col3\" class=\"data row5 col3\" >1898.2498</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.9634</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.1024</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col6\" class=\"data row5 col6\" >0.0770</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow5_col7\" class=\"data row5 col7\" >0.2429</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col0\" class=\"data row6 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col1\" class=\"data row6 col1\" >998.2558</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col2\" class=\"data row6 col2\" >5292680.3084</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col3\" class=\"data row6 col3\" >2225.8934</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.9479</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.1078</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col6\" class=\"data row6 col6\" >0.0773</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow6_col7\" class=\"data row6 col7\" >0.0184</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col1\" class=\"data row7 col1\" >2413.5865</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col2\" class=\"data row7 col2\" >14120531.3503</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col3\" class=\"data row7 col3\" >3726.1698</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.8621</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6689</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col6\" class=\"data row7 col6\" >0.2875</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow7_col7\" class=\"data row7 col7\" >0.0032</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col0\" class=\"data row8 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col1\" class=\"data row8 col1\" >2412.1923</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col2\" class=\"data row8 col2\" >14246796.4091</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col3\" class=\"data row8 col3\" >3744.2303</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.8608</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.6767</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col6\" class=\"data row8 col6\" >0.2866</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow8_col7\" class=\"data row8 col7\" >0.0632</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col1\" class=\"data row9 col1\" >2414.2906</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col2\" class=\"data row9 col2\" >14243122.1076</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col3\" class=\"data row9 col3\" >3744.1362</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.8607</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.6652</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col6\" class=\"data row9 col6\" >0.2871</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow9_col7\" class=\"data row9 col7\" >0.0112</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col0\" class=\"data row10 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col1\" class=\"data row10 col1\" >2355.6147</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col2\" class=\"data row10 col2\" >14272022.0783</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col3\" class=\"data row10 col3\" >3745.3096</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.8607</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.6391</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col6\" class=\"data row10 col6\" >0.2728</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow10_col7\" class=\"data row10 col7\" >0.0061</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col0\" class=\"data row11 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col1\" class=\"data row11 col1\" >2415.8088</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col2\" class=\"data row11 col2\" >14270819.0700</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col3\" class=\"data row11 col3\" >3747.0008</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.8606</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.6696</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col6\" class=\"data row11 col6\" >0.2873</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow11_col7\" class=\"data row11 col7\" >0.0109</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col0\" class=\"data row12 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col1\" class=\"data row12 col1\" >2442.0741</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col2\" class=\"data row12 col2\" >14402872.8948</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col3\" class=\"data row12 col3\" >3762.4955</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col4\" class=\"data row12 col4\" >0.8593</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col5\" class=\"data row12 col5\" >0.6912</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col6\" class=\"data row12 col6\" >0.2913</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow12_col7\" class=\"data row12 col7\" >0.0088</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col0\" class=\"data row13 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col1\" class=\"data row13 col1\" >2125.1496</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col2\" class=\"data row13 col2\" >15953022.4014</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col3\" class=\"data row13 col3\" >3932.8637</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col4\" class=\"data row13 col4\" >0.8466</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col5\" class=\"data row13 col5\" >0.4871</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col6\" class=\"data row13 col6\" >0.2174</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow13_col7\" class=\"data row13 col7\" >1.4191</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col0\" class=\"data row14 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col1\" class=\"data row14 col1\" >1935.9940</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col2\" class=\"data row14 col2\" >18586352.5149</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col3\" class=\"data row14 col3\" >4251.5931</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col4\" class=\"data row14 col4\" >0.8210</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col5\" class=\"data row14 col5\" >0.4332</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col6\" class=\"data row14 col6\" >0.1657</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow14_col7\" class=\"data row14 col7\" >0.0962</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col0\" class=\"data row15 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col1\" class=\"data row15 col1\" >1942.7078</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col2\" class=\"data row15 col2\" >19981884.1681</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col3\" class=\"data row15 col3\" >4402.2962</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col4\" class=\"data row15 col4\" >0.8081</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col5\" class=\"data row15 col5\" >0.4267</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col6\" class=\"data row15 col6\" >0.1587</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow15_col7\" class=\"data row15 col7\" >0.0239</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col0\" class=\"data row16 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col1\" class=\"data row16 col1\" >2792.7312</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col2\" class=\"data row16 col2\" >23728654.5267</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col3\" class=\"data row16 col3\" >4829.3171</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col4\" class=\"data row16 col4\" >0.7678</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col5\" class=\"data row16 col5\" >0.5818</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col6\" class=\"data row16 col6\" >0.2654</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow16_col7\" class=\"data row16 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col0\" class=\"data row17 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col1\" class=\"data row17 col1\" >4168.3683</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col2\" class=\"data row17 col2\" >24904927.4790</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col3\" class=\"data row17 col3\" >4978.5520</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col4\" class=\"data row17 col4\" >0.7497</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col5\" class=\"data row17 col5\" >0.5008</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col6\" class=\"data row17 col6\" >0.5824</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow17_col7\" class=\"data row17 col7\" >0.2011</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col0\" class=\"data row18 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col1\" class=\"data row18 col1\" >2972.7947</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col2\" class=\"data row18 col2\" >29642550.7216</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col3\" class=\"data row18 col3\" >5424.0297</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col4\" class=\"data row18 col4\" >0.7048</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col5\" class=\"data row18 col5\" >0.3671</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col6\" class=\"data row18 col6\" >0.2742</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow18_col7\" class=\"data row18 col7\" >0.0245</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col0\" class=\"data row19 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col1\" class=\"data row19 col1\" >5029.7513</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col2\" class=\"data row19 col2\" >56400091.2389</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col3\" class=\"data row19 col3\" >7467.6909</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col4\" class=\"data row19 col4\" >0.4472</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col5\" class=\"data row19 col5\" >0.5369</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col6\" class=\"data row19 col6\" >0.5845</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow19_col7\" class=\"data row19 col7\" >0.0057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col0\" class=\"data row20 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col1\" class=\"data row20 col1\" >6438.9640</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col2\" class=\"data row20 col2\" >115335543.3727</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col3\" class=\"data row20 col3\" >10711.3800</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col4\" class=\"data row20 col4\" >-0.1424</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col5\" class=\"data row20 col5\" >0.7138</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col6\" class=\"data row20 col6\" >0.5282</td>\n",
       "                        <td id=\"T_3caaaf4a_e630_11ea_a499_482ae32b83darow20_col7\" class=\"data row20 col7\" >0.6039</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b55abe548>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models(blacklist = ['ransac'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "epD0BEVyzZrr"
   },
   "source": [
    "Two simple words of code ***(not even a line)*** have trained and evaluated over 20 models using cross validation. The score grid printed above highlights the highest performing metric for comparison purposes only. The grid by default is sorted using `R2` (highest to lowest) which can be changed by passing `sort` parameter. For example `compare_models(sort = 'RMSLE')` will sort the grid by RMSLE (lower to higher since lower is better). If you want to change the fold parameter from the default value of `10` to a different value then you can use the `fold` parameter. For example `compare_models(fold = 5)` will compare all models on 5 fold cross validation. Reducing the number of folds will improve the training time. By default, compare_models return the best performing model based on default sort order but can be used to return a list of top N models by using `n_select` parameter. </br>\n",
    "\n",
    "Notice that how `blacklist` parameter is used to block certain models (in this case `RANSAC`). In the next release of PyCaret `2.1`, `blacklist` parameter is replaced with the term `exclude` and `whitelist` parameter will be replaced with the term `include`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzpBazV1zZrx"
   },
   "source": [
    "# 8.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPqPRp5OzZr1"
   },
   "source": [
    "`create_model` is the most granular function in PyCaret and is often the foundation behind most of the PyCaret functionalities. As the name suggests this function trains and evaluates a model using cross validation that can be set with fold parameter. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. \n",
    "\n",
    "For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.\n",
    "\n",
    "- AdaBoost Regressor ('ada')\n",
    "- Light Gradient Boosting Machine ('lightgbm') \n",
    "- Decision Tree\t ('dt')\n",
    "\n",
    "There are 25 regressors available in the model library of PyCaret. To see list of all regressors either check the docstring or use `models` function to see the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>sklearn.linear_model.LinearRegression</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>sklearn.linear_model.Lasso</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>sklearn.linear_model.Ridge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>sklearn.linear_model.ElasticNet</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lar</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model.Lars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llar</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model.LassoLars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omp</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>sklearn.linear_model.OMP</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>sklearn.linear_model.BayesianRidge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ard</th>\n",
       "      <td>Automatic Relevance Determination</td>\n",
       "      <td>sklearn.linear_model.ARDRegression</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>par</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>sklearn.linear_model.PAR</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ransac</th>\n",
       "      <td>Random Sample Consensus</td>\n",
       "      <td>sklearn.linear_model.RANSACRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>TheilSen Regressor</td>\n",
       "      <td>sklearn.linear_model.TheilSenRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>sklearn.linear_model.HuberRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kr</th>\n",
       "      <td>Kernel Ridge</td>\n",
       "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>sklearn.svm.SVR</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>sklearn.neighbors.KNeighborsRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>sklearn.tree.DecisionTreeRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>sklearn.ensemble.RandomForestRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>sklearn.ensemble.ExtraTreesRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>sklearn.ensemble.AdaBoostRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbr</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>sklearn.ensemble.GradientBoostingRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>Multi Level Perceptron</td>\n",
       "      <td>sklearn.neural_network.MLPRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.readthedocs.io</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>github.com/microsoft/LightGBM</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>https://catboost.ai</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "ID                                            \n",
       "lr                        Linear Regression   \n",
       "lasso                      Lasso Regression   \n",
       "ridge                      Ridge Regression   \n",
       "en                              Elastic Net   \n",
       "lar                  Least Angle Regression   \n",
       "llar           Lasso Least Angle Regression   \n",
       "omp             Orthogonal Matching Pursuit   \n",
       "br                           Bayesian Ridge   \n",
       "ard       Automatic Relevance Determination   \n",
       "par            Passive Aggressive Regressor   \n",
       "ransac              Random Sample Consensus   \n",
       "tr                       TheilSen Regressor   \n",
       "huber                       Huber Regressor   \n",
       "kr                             Kernel Ridge   \n",
       "svm                  Support Vector Machine   \n",
       "knn                   K Neighbors Regressor   \n",
       "dt                            Decision Tree   \n",
       "rf                            Random Forest   \n",
       "et                    Extra Trees Regressor   \n",
       "ada                      AdaBoost Regressor   \n",
       "gbr             Gradient Boosting Regressor   \n",
       "mlp                  Multi Level Perceptron   \n",
       "xgboost           Extreme Gradient Boosting   \n",
       "lightgbm    Light Gradient Boosting Machine   \n",
       "catboost                 CatBoost Regressor   \n",
       "\n",
       "                                           Reference  Turbo  \n",
       "ID                                                           \n",
       "lr             sklearn.linear_model.LinearRegression   True  \n",
       "lasso                     sklearn.linear_model.Lasso   True  \n",
       "ridge                     sklearn.linear_model.Ridge   True  \n",
       "en                   sklearn.linear_model.ElasticNet   True  \n",
       "lar                        sklearn.linear_model.Lars   True  \n",
       "llar                  sklearn.linear_model.LassoLars   True  \n",
       "omp                         sklearn.linear_model.OMP   True  \n",
       "br                sklearn.linear_model.BayesianRidge   True  \n",
       "ard               sklearn.linear_model.ARDRegression  False  \n",
       "par                         sklearn.linear_model.PAR   True  \n",
       "ransac          sklearn.linear_model.RANSACRegressor   True  \n",
       "tr            sklearn.linear_model.TheilSenRegressor   True  \n",
       "huber            sklearn.linear_model.HuberRegressor   True  \n",
       "kr                  sklearn.kernel_ridge.KernelRidge  False  \n",
       "svm                                  sklearn.svm.SVR   True  \n",
       "knn            sklearn.neighbors.KNeighborsRegressor   True  \n",
       "dt                sklearn.tree.DecisionTreeRegressor   True  \n",
       "rf            sklearn.ensemble.RandomForestRegressor   True  \n",
       "et              sklearn.ensemble.ExtraTreesRegressor   True  \n",
       "ada               sklearn.ensemble.AdaBoostRegressor   True  \n",
       "gbr       sklearn.ensemble.GradientBoostingRegressor   True  \n",
       "mlp              sklearn.neural_network.MLPRegressor  False  \n",
       "xgboost                       xgboost.readthedocs.io   True  \n",
       "lightgbm               github.com/microsoft/LightGBM   True  \n",
       "catboost                         https://catboost.ai   True  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxKHHQcbzZr5"
   },
   "source": [
    "### 8.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:30.940588Z",
     "start_time": "2020-04-23T13:22:28.992149Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "-NVGDCR3zZr8",
    "outputId": "06f5fc68-d2a5-4b59-fea2-3661ea1cb29d"
   },
   "outputs": [
    {
     "data": {
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       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow1_col1\" class=\"data row1 col1\" >29522153.3063</td>\n",
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       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow2_col1\" class=\"data row2 col1\" >24626666.3376</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow2_col2\" class=\"data row2 col2\" >4962.5262</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.7741</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.5269</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.6318</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow3_col0\" class=\"data row3 col0\" >4201.9811</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow3_col1\" class=\"data row3 col1\" >22493390.2282</td>\n",
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       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
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       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow4_col1\" class=\"data row4 col1\" >23358876.5338</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow4_col2\" class=\"data row4 col2\" >4833.1022</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.7087</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.4527</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.5011</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col0\" class=\"data row5 col0\" >4565.6610</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col1\" class=\"data row5 col1\" >29732993.3263</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col2\" class=\"data row5 col2\" >5452.7968</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.7462</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.5462</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.6598</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col0\" class=\"data row6 col0\" >4684.0814</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col1\" class=\"data row6 col1\" >28516379.6188</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col2\" class=\"data row6 col2\" >5340.0730</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.7321</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.5567</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.6716</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col0\" class=\"data row7 col0\" >4446.0672</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col1\" class=\"data row7 col1\" >27422298.8102</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col2\" class=\"data row7 col2\" >5236.6305</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.6532</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.5217</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.6138</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col0\" class=\"data row8 col0\" >3870.3897</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col1\" class=\"data row8 col1\" >20611456.9747</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col2\" class=\"data row8 col2\" >4539.9842</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.7976</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.4664</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.5264</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col0\" class=\"data row9 col0\" >3667.6258</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col1\" class=\"data row9 col1\" >20126723.1218</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col2\" class=\"data row9 col2\" >4486.2817</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8084</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.4869</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.5520</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col0\" class=\"data row10 col0\" >4168.3683</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col1\" class=\"data row10 col1\" >24904927.4790</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col2\" class=\"data row10 col2\" >4978.5520</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.7497</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.5008</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.5824</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col0\" class=\"data row11 col0\" >351.9637</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col1\" class=\"data row11 col1\" >3445207.0216</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col2\" class=\"data row11 col2\" >344.8881</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0427</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0337</td>\n",
       "                        <td id=\"T_ad87e166_e633_11ea_ad9c_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0560</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b5e5a0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = create_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:38.337909Z",
     "start_time": "2020-04-23T13:22:38.335103Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "NHL2zciizZsI",
    "outputId": "d606ad03-ecd5-487b-b205-bfc7a841f3a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
      "                  n_estimators=50, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "print(ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-dvDHxCzZsU"
   },
   "source": [
    "### 8.2 Light Gradient Boosting Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:46.292921Z",
     "start_time": "2020-04-23T13:22:43.713857Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "NC7OVDVrzZsX",
    "outputId": "a5abc702-d270-4134-892a-e9ecf82bdebb"
   },
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_af955ed4_e633_11ea_beb2_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col0\" class=\"data row0 col0\" >625.1813</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col1\" class=\"data row0 col1\" >1051762.9578</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col2\" class=\"data row0 col2\" >1025.5550</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.9885</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.0715</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0526</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col0\" class=\"data row1 col0\" >797.6185</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col1\" class=\"data row1 col1\" >5638866.1771</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col2\" class=\"data row1 col2\" >2374.6297</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.9568</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.0727</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0537</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col0\" class=\"data row2 col0\" >825.0215</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col1\" class=\"data row2 col1\" >3318727.1968</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col2\" class=\"data row2 col2\" >1821.7374</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9696</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0857</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0616</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col0\" class=\"data row3 col0\" >720.3923</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col1\" class=\"data row3 col1\" >1697211.3816</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col2\" class=\"data row3 col2\" >1302.7707</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9813</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.0714</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0554</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col0\" class=\"data row4 col0\" >645.6800</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col1\" class=\"data row4 col1\" >1799949.1196</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col2\" class=\"data row4 col2\" >1341.6218</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.9775</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.0745</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0534</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col0\" class=\"data row5 col0\" >830.7176</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col1\" class=\"data row5 col1\" >6423604.0184</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col2\" class=\"data row5 col2\" >2534.4830</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9452</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.0810</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col0\" class=\"data row6 col0\" >800.2611</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col1\" class=\"data row6 col1\" >3355855.9955</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col2\" class=\"data row6 col2\" >1831.8996</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9685</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0793</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0585</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col0\" class=\"data row7 col0\" >714.3607</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col1\" class=\"data row7 col1\" >1930222.6458</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col2\" class=\"data row7 col2\" >1389.3245</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9756</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0732</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0556</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col0\" class=\"data row8 col0\" >784.7648</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col1\" class=\"data row8 col1\" >2211933.1546</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col2\" class=\"data row8 col2\" >1487.2569</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9783</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.0766</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0582</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col0\" class=\"data row9 col0\" >778.3590</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col1\" class=\"data row9 col1\" >3127561.3571</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col2\" class=\"data row9 col2\" >1768.4913</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9702</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.0872</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0609</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col0\" class=\"data row10 col0\" >752.2357</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col1\" class=\"data row10 col1\" >3055569.4004</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col2\" class=\"data row10 col2\" >1687.7770</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9711</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.0773</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af955ed4_e633_11ea_beb2_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col0\" class=\"data row11 col0\" >68.9270</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col1\" class=\"data row11 col1\" >1661227.6431</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col2\" class=\"data row11 col2\" >454.9486</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0119</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0055</td>\n",
       "                        <td id=\"T_af955ed4_e633_11ea_beb2_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0029</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b55a427c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm = create_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8DvIuOrzZsm"
   },
   "source": [
    "### 8.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:50.364299Z",
     "start_time": "2020-04-23T13:22:50.002325Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "1Y_Czm6xzZsr",
    "outputId": "df27ebb4-257b-440e-cd9b-de05ba0c0f35"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9735</td>\n",
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       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow3_col1\" class=\"data row3 col1\" >4493663.0927</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow4_col0\" class=\"data row4 col0\" >1109.0019</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow4_col1\" class=\"data row4 col1\" >9374845.7114</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow4_col2\" class=\"data row4 col2\" >3061.8370</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.8831</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.1185</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col0\" class=\"data row5 col0\" >1070.6609</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col1\" class=\"data row5 col1\" >7961620.3151</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col2\" class=\"data row5 col2\" >2821.6343</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9320</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.1124</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0813</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col0\" class=\"data row6 col0\" >988.0220</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col1\" class=\"data row6 col1\" >4026193.9849</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col2\" class=\"data row6 col2\" >2006.5378</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9622</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.1060</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0781</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col0\" class=\"data row7 col0\" >842.4033</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col1\" class=\"data row7 col1\" >2075226.6798</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col2\" class=\"data row7 col2\" >1440.5647</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9738</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0972</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0730</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col0\" class=\"data row8 col0\" >963.7586</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col1\" class=\"data row8 col1\" >4253092.9747</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col2\" class=\"data row8 col2\" >2062.3028</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9582</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.1044</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0727</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col0\" class=\"data row9 col0\" >1047.4405</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col1\" class=\"data row9 col1\" >5085467.3011</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col2\" class=\"data row9 col2\" >2255.0981</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9516</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.1255</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0845</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col0\" class=\"data row10 col0\" >998.2558</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col1\" class=\"data row10 col1\" >5292680.3084</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col2\" class=\"data row10 col2\" >2225.8934</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9479</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.1078</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0773</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afe71776_e633_11ea_aa47_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col0\" class=\"data row11 col0\" >94.7918</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col1\" class=\"data row11 col1\" >2743262.3114</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col2\" class=\"data row11 col2\" >581.4457</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0272</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0085</td>\n",
       "                        <td id=\"T_afe71776_e633_11ea_aa47_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0042</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b57567208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NsOBIl8szZs1"
   },
   "source": [
    "Notice that the Mean score of all models matches with the score printed in `compare_models()`. This is because the metrics printed in the `compare_models()` score grid are the average scores across all CV folds. Similar to `compare_models()`, if you want to change the fold parameter from the default value of 10 to a different value then you can use the `fold` parameter. For Example: `create_model('dt', fold = 5)` to create Decision Tree using 5 fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RZB8YllzZs7"
   },
   "source": [
    "# 9.0 Tune a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AYYWC1X5zZs-"
   },
   "source": [
    "When a model is created using the `create_model` function it uses the default hyperparameters to train the model. In order to tune hyperparameters, the `tune_model` function is used. This function automatically tunes the hyperparameters of a model using `Random Grid Search` on a pre-defined search space. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. To use the custom search grid, you can pass `custom_grid` parameter in the `tune_model` function (see 9.2 LightGBM tuning below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uUSmZLGzZtB"
   },
   "source": [
    "### 9.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:23.226986Z",
     "start_time": "2020-04-23T13:25:17.516114Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "XM7qgcGIzZtE",
    "outputId": "e87c7fac-4dfe-4733-ddc5-d44359b0ea0d"
   },
   "outputs": [
    {
     "data": {
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       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow0_col0\" class=\"data row0 col0\" >2799.3407</td>\n",
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       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow1_col0\" class=\"data row1 col0\" >2966.6977</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow1_col1\" class=\"data row1 col1\" >23597057.7619</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow1_col2\" class=\"data row1 col2\" >4857.6803</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.8192</td>\n",
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       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col0\" class=\"data row2 col0\" >2566.6406</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col1\" class=\"data row2 col1\" >15658252.5578</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col2\" class=\"data row2 col2\" >3957.0510</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.8564</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.2610</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.2364</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col0\" class=\"data row3 col0\" >2602.3745</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col1\" class=\"data row3 col1\" >14383448.2182</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col2\" class=\"data row3 col2\" >3792.5517</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.8413</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.2709</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.2326</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col0\" class=\"data row4 col0\" >2583.1811</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col1\" class=\"data row4 col1\" >14185346.1705</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col2\" class=\"data row4 col2\" >3766.3439</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.8231</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.3012</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.2757</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col0\" class=\"data row5 col0\" >2739.3273</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col1\" class=\"data row5 col1\" >20428026.4570</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col2\" class=\"data row5 col2\" >4519.7374</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.8257</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.3061</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.2866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col0\" class=\"data row6 col0\" >2776.9122</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col1\" class=\"data row6 col1\" >17107851.9502</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col2\" class=\"data row6 col2\" >4136.1639</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.8393</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.3053</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.2834</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col0\" class=\"data row7 col0\" >2888.0504</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col1\" class=\"data row7 col1\" >17889532.1511</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col2\" class=\"data row7 col2\" >4229.6019</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.7738</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.3114</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.2962</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col0\" class=\"data row8 col0\" >2639.0625</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col1\" class=\"data row8 col1\" >15672432.4243</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col2\" class=\"data row8 col2\" >3958.8423</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.8461</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.3050</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.2815</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col0\" class=\"data row9 col0\" >2620.6692</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col1\" class=\"data row9 col1\" >16400041.6306</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col2\" class=\"data row9 col2\" >4049.6965</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.8438</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.3131</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.2951</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col0\" class=\"data row10 col0\" >2718.2256</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col1\" class=\"data row10 col1\" >17176102.1352</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col2\" class=\"data row10 col2\" >4132.2176</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.8288</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.2977</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.2759</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col0\" class=\"data row11 col0\" >130.9460</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col1\" class=\"data row11 col1\" >2734433.9955</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col2\" class=\"data row11 col2\" >317.6152</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0219</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0173</td>\n",
       "                        <td id=\"T_dc2363a2_e633_11ea_a9aa_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0222</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b5755f6c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_ada = tune_model(ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:31.211932Z",
     "start_time": "2020-04-23T13:25:31.208661Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "Ul0HJFoRzZtU",
    "outputId": "5ac4ab0b-c746-4a9f-a286-3e5cd2bbe536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.1, loss='exponential',\n",
      "                  n_estimators=110, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "print(tuned_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kvdvfdUzZtj"
   },
   "source": [
    "### 9.2 Light Gradient Boosting Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "lgbm_params = {'num_leaves': np.arange(10,200,10),\n",
    "                        'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],\n",
    "                        'learning_rate': np.arange(0.1,1,0.1)\n",
    "                        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:35.696328Z",
     "start_time": "2020-04-23T13:25:33.725433Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "s1agvmDFzZtm",
    "outputId": "7cfab1a7-e7c2-40df-ad1e-a40067bcc4e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_243a7a74_e634_11ea_bce3_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col0\" class=\"data row0 col0\" >733.2214</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col1\" class=\"data row0 col1\" >1464748.5361</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col2\" class=\"data row0 col2\" >1210.2680</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.9839</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.0892</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow0_col5\" class=\"data row0 col5\" >0.0646</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col0\" class=\"data row1 col0\" >840.4463</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col1\" class=\"data row1 col1\" >2888551.4272</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col2\" class=\"data row1 col2\" >1699.5739</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.9779</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.0851</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow1_col5\" class=\"data row1 col5\" >0.0615</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col0\" class=\"data row2 col0\" >849.6549</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col1\" class=\"data row2 col1\" >2889877.5013</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col2\" class=\"data row2 col2\" >1699.9640</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9735</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0933</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0685</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col0\" class=\"data row3 col0\" >776.6915</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col1\" class=\"data row3 col1\" >1848027.8385</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col2\" class=\"data row3 col2\" >1359.4219</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9796</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.0815</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0618</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col0\" class=\"data row4 col0\" >835.3916</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col1\" class=\"data row4 col1\" >3506099.9656</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col2\" class=\"data row4 col2\" >1872.4583</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.9563</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.0948</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0659</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col0\" class=\"data row5 col0\" >832.9681</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col1\" class=\"data row5 col1\" >4295831.4477</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col2\" class=\"data row5 col2\" >2072.6388</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9633</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.0860</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0651</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col0\" class=\"data row6 col0\" >839.3671</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col1\" class=\"data row6 col1\" >2728995.5293</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col2\" class=\"data row6 col2\" >1651.9672</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9744</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0854</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0638</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col0\" class=\"data row7 col0\" >910.5734</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col1\" class=\"data row7 col1\" >3803403.3788</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col2\" class=\"data row7 col2\" >1950.2316</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9519</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0937</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0684</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col0\" class=\"data row8 col0\" >850.0181</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col1\" class=\"data row8 col1\" >2616323.5939</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col2\" class=\"data row8 col2\" >1617.5054</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9743</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.0852</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0644</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col0\" class=\"data row9 col0\" >792.2154</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col1\" class=\"data row9 col1\" >3304799.7438</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col2\" class=\"data row9 col2\" >1817.9108</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9685</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.0965</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col0\" class=\"data row10 col0\" >826.0548</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col1\" class=\"data row10 col1\" >2934665.8962</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col2\" class=\"data row10 col2\" >1695.1940</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9704</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.0890</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_243a7a74_e634_11ea_bce3_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col0\" class=\"data row11 col0\" >45.8190</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col1\" class=\"data row11 col1\" >809952.2463</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col2\" class=\"data row11 col2\" >246.9480</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0098</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0049</td>\n",
       "                        <td id=\"T_243a7a74_e634_11ea_bce3_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0022</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b57567ec8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lightgbm = tune_model(lightgbm, custom_grid = lgbm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.7000000000000001,\n",
      "              max_depth=90, min_child_samples=20, min_child_weight=0.001,\n",
      "              min_split_gain=0.0, n_estimators=100, n_jobs=-1, num_leaves=10,\n",
      "              objective=None, random_state=123, reg_alpha=0.0, reg_lambda=0.0,\n",
      "              silent=True, subsample=1.0, subsample_for_bin=200000,\n",
      "              subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "print(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ovz73MkgzZtx"
   },
   "source": [
    "### 9.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:38.913881Z",
     "start_time": "2020-04-23T13:25:38.267918Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "kFImcOpXzZt0",
    "outputId": "36165e01-0b2e-4fa4-efcc-5efa70a3236f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        }</style><table id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow0_col0\" class=\"data row0 col0\" >934.9213</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow0_col1\" class=\"data row0 col1\" >3637475.4401</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow0_col2\" class=\"data row0 col2\" >1907.2167</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow0_col3\" class=\"data row0 col3\" >0.9601</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow0_col4\" class=\"data row0 col4\" >0.0965</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow1_col0\" class=\"data row1 col0\" >1016.3795</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow1_col1\" class=\"data row1 col1\" >6295426.0355</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow1_col2\" class=\"data row1 col2\" >2509.0688</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow1_col3\" class=\"data row1 col3\" >0.9518</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow1_col4\" class=\"data row1 col4\" >0.1012</td>\n",
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       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col0\" class=\"data row2 col0\" >888.6061</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col1\" class=\"data row2 col1\" >2887457.8482</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col2\" class=\"data row2 col2\" >1699.2521</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col3\" class=\"data row2 col3\" >0.9735</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col4\" class=\"data row2 col4\" >0.0990</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow2_col5\" class=\"data row2 col5\" >0.0734</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col0\" class=\"data row3 col0\" >899.5514</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col1\" class=\"data row3 col1\" >3106659.0817</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col2\" class=\"data row3 col2\" >1762.5717</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col3\" class=\"data row3 col3\" >0.9657</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col4\" class=\"data row3 col4\" >0.0993</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow3_col5\" class=\"data row3 col5\" >0.0739</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col0\" class=\"data row4 col0\" >1058.6666</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col1\" class=\"data row4 col1\" >8213834.5359</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col2\" class=\"data row4 col2\" >2865.9788</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col3\" class=\"data row4 col3\" >0.8976</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col4\" class=\"data row4 col4\" >0.1185</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow4_col5\" class=\"data row4 col5\" >0.0804</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col0\" class=\"data row5 col0\" >1061.3095</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col1\" class=\"data row5 col1\" >8084798.8028</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col2\" class=\"data row5 col2\" >2843.3781</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col3\" class=\"data row5 col3\" >0.9310</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col4\" class=\"data row5 col4\" >0.1105</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow5_col5\" class=\"data row5 col5\" >0.0745</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col0\" class=\"data row6 col0\" >880.2731</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col1\" class=\"data row6 col1\" >2693970.8756</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col2\" class=\"data row6 col2\" >1641.3320</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col3\" class=\"data row6 col3\" >0.9747</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col4\" class=\"data row6 col4\" >0.0941</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow6_col5\" class=\"data row6 col5\" >0.0716</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col0\" class=\"data row7 col0\" >909.6749</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col1\" class=\"data row7 col1\" >3504336.0198</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col2\" class=\"data row7 col2\" >1871.9872</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col3\" class=\"data row7 col3\" >0.9557</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col4\" class=\"data row7 col4\" >0.0972</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow7_col5\" class=\"data row7 col5\" >0.0713</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col0\" class=\"data row8 col0\" >990.9343</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col1\" class=\"data row8 col1\" >3830000.8804</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col2\" class=\"data row8 col2\" >1957.0388</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col3\" class=\"data row8 col3\" >0.9624</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col4\" class=\"data row8 col4\" >0.1036</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow8_col5\" class=\"data row8 col5\" >0.0762</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col0\" class=\"data row9 col0\" >1038.9525</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col1\" class=\"data row9 col1\" >5476260.4232</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col2\" class=\"data row9 col2\" >2340.1411</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col3\" class=\"data row9 col3\" >0.9479</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col4\" class=\"data row9 col4\" >0.1143</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow9_col5\" class=\"data row9 col5\" >0.0795</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col0\" class=\"data row10 col0\" >967.9269</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col1\" class=\"data row10 col1\" >4773021.9943</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col2\" class=\"data row10 col2\" >2139.7965</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col3\" class=\"data row10 col3\" >0.9520</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col4\" class=\"data row10 col4\" >0.1034</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow10_col5\" class=\"data row10 col5\" >0.0751</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col0\" class=\"data row11 col0\" >69.2882</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col1\" class=\"data row11 col1\" >2001554.5181</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col2\" class=\"data row11 col2\" >440.7866</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col3\" class=\"data row11 col3\" >0.0219</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col4\" class=\"data row11 col4\" >0.0078</td>\n",
       "                        <td id=\"T_2e3e77a4_e634_11ea_b1dd_482ae32b83darow11_col5\" class=\"data row11 col5\" >0.0028</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23b5755b448>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_dt = tune_model(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TcSjA7SUzZt-"
   },
   "source": [
    "By default, `tune_model` optimizes `R2` but this can be changed using optimize parameter. For example: tune_model(dt, optimize = 'MAE') will search for the hyperparameters of a Decision Tree Regressor that results in the lowest `MAE` instead of highest `R2`. For the purposes of this example, we have used the default metric `R2` for the sake of simplicity only. The methodology behind selecting the right metric to evaluate a regressor is beyond the scope of this tutorial but if you would like to learn more about it, you can __[click here](https://www.dataquest.io/blog/understanding-regression-error-metrics/)__ to develop an understanding on regression error metrics.\n",
    "\n",
    "Metrics alone are not the only criteria you should consider when finalizing the best model for production. Other factors to consider include training time, standard deviation of k-folds etc. As you progress through the tutorial series we will discuss those factors in detail at the intermediate and expert levels. For now, let's move forward considering the Tuned Light Gradient Boosting Machine stored in the `tuned_lightgbm` variable as our best model for the remainder of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HR-mHgtCzZuE"
   },
   "source": [
    "# 10.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N6i7Ggg_zZuH"
   },
   "source": [
    "Before model finalization, the `plot_model()` function can be used to analyze the performance across different aspects such as Residuals Plot, Prediction Error, Feature Importance etc. This function takes a trained model object and returns a plot based on the test / hold-out set. \n",
    "\n",
    "There are over 10 plots available, please see the `plot_model()` docstring for the list of available plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJCYRQj9zZuU"
   },
   "source": [
    "### 10.1 Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:47.857901Z",
     "start_time": "2020-04-23T13:29:47.261824Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ml-qe8dTzZuX",
    "outputId": "4c69b0b8-8e82-4003-f0e2-158b19a79f34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rM9dWgfVzZuh"
   },
   "source": [
    "### 10.2 Prediction Error Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:53.402735Z",
     "start_time": "2020-04-23T13:29:53.197809Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GPwWRYehzZuk",
    "outputId": "e3dfe255-fa08-42f7-e556-74c7909f6e6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot = 'error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWu_EtTGzZuu"
   },
   "source": [
    "### 10.3 Feature Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:57.854731Z",
     "start_time": "2020-04-23T13:29:57.758312Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "7Yh852PPzZux",
    "outputId": "38295169-5000-4a07-e71d-76de9877ab42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot='feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI_tk-8RzZu8"
   },
   "source": [
    "*Another* way to analyze the performance of models is to use the `evaluate_model()` function which displays a user interface for all of the available plots for a given model. It internally uses the `plot_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:30:17.357283Z",
     "start_time": "2020-04-23T13:30:17.326916Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398,
     "referenced_widgets": [
      "2f905c9057e849cfb68d8b4a73a9ae2c",
      "4705ae97c5c341db8a7efcc7e4334c79",
      "202f5f0dfbea4f6cb6a75e4aebd370c7",
      "4c0b5f15356140e9bd6ed3a2e0850baa",
      "63e7b6e04e3d43118698abaca0973960",
      "b7731479f5f141289a4939cb73adfb28",
      "5eb7949ac4a140118e9ee6be49921284",
      "19ea86ac89a349e281011fbd327c29c0",
      "c9bbc67e75d1477e8cd7a64502b98704",
      "5334e09f4bed4a0ba45635300e76027e",
      "14244d0fca4f40a8b3552675869748a3",
      "baf068abf86f44a9a9502f79dfe17eb5",
      "3495c4e24f1d4b22af1117ea2403a4fa",
      "83a10da0205742d9a383671a0be02606",
      "4ae774bce02d4c1ab1351cd75b25cf17",
      "ee5416de1bb1462e87b2fa6f9065d646",
      "36a9c708b65e498586a07d526e6a5d06",
      "aeb87dce945f4d588807575e9f6e64c3"
     ]
    },
    "colab_type": "code",
    "id": "J4ryBACHzZu_",
    "outputId": "246ab553-8043-4cbe-fe99-7ea00bb13aed"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2731df8c285d4f3cb015f7afc8e43c09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CxKARgKAzZvJ"
   },
   "source": [
    "# 11.0 Predict on Test / Hold-out Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8k_rmplzZvL"
   },
   "source": [
    "Before finalizing the model, it is advisable to perform one final check by predicting the test/hold-out set and reviewing the evaluation metrics. If you look at the information grid in Section 6 above, you will see that 30% (1621 samples) of the data has been separated out as a test/hold-out sample. All of the evaluation metrics we have seen above are cross-validated results based on training set (70%) only. Now, using our final trained model stored in the `tuned_lightgbm` variable we will predict the hold-out sample and evaluate the metrics to see if they are materially different than the CV results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:31:47.571061Z",
     "start_time": "2020-04-23T13:31:47.543390Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ozTyeSjCzZvY",
    "outputId": "1cdf25f4-0988-40fa-c8e9-5695fec11f05"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>815.5947</td>\n",
       "      <td>3.533081e+06</td>\n",
       "      <td>1879.6493</td>\n",
       "      <td>0.9678</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>0.0627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE           MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  815.5947  3.533081e+06  1879.6493  0.9678   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0926  0.0627  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(tuned_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UouOXHaxzZvo"
   },
   "source": [
    "The R2 on the test/hold-out set is **`0.9678`** compared to **`0.9704`** achieved on `tuned_lightgbm` CV results (in section 9.2 above). This is not a significant difference. If there is a large variation between the test/hold-out and CV results, then this would normally indicate over-fitting but could also be due to several other factors and would require further investigation. In this case, we will move forward with finalizing the model and predicting on unseen data (the 10% that we had separated in the beginning and never exposed to PyCaret).\n",
    "\n",
    "(TIP : It's always good to look at the standard deviation of CV results when using `create_model`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0PmhEQFzZvr"
   },
   "source": [
    "# 12.0 Finalize Model for Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rtaj0uWgzZvx"
   },
   "source": [
    "Model finalization is the last step in the experiment. A normal machine learning workflow in PyCaret starts with `setup()`, followed by comparing all models using `compare_models()` and shortlisting a few candidate models (based on the metric of interest) to perform several modeling techniques such as hyperparameter tuning, ensembling, stacking etc. This workflow will eventually lead you to the best model for use in making predictions on new and unseen data. The `finalize_model()` function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:51.300384Z",
     "start_time": "2020-04-23T13:33:51.215142Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "UPk310pezZv0"
   },
   "outputs": [],
   "source": [
    "final_lightgbm = finalize_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:52.258663Z",
     "start_time": "2020-04-23T13:33:52.255298Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IGFUGDAPzZwF",
    "outputId": "ddaa33c4-f728-444a-fc33-47da5c37304a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.7000000000000001,\n",
      "              max_depth=90, min_child_samples=20, min_child_weight=0.001,\n",
      "              min_split_gain=0.0, n_estimators=100, n_jobs=-1, num_leaves=10,\n",
      "              objective=None, random_state=123, reg_alpha=0.0, reg_lambda=0.0,\n",
      "              silent=True, subsample=1.0, subsample_for_bin=200000,\n",
      "              subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "print(final_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmJjaIkrzZwQ"
   },
   "source": [
    "**Caution:** One final word of caution. Once the model is finalized using `finalize_model()`, the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after `finalize_model()` is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use `final_lightgbm` under `predict_model()` to compare the information grid with the one above in section 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:34:02.730481Z",
     "start_time": "2020-04-23T13:34:02.704786Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bmYJRTAyzZwU",
    "outputId": "58e2f57b-abda-4166-c82b-af52ab6f18b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>563.6509</td>\n",
       "      <td>1.128173e+06</td>\n",
       "      <td>1062.1547</td>\n",
       "      <td>0.9897</td>\n",
       "      <td>0.0672</td>\n",
       "      <td>0.0494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE           MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  563.6509  1.128173e+06  1062.1547  0.9897   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0672  0.0494  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(final_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NkpL1ZHzZwr"
   },
   "source": [
    "Notice how the R2 in the `final_lightgbm` has increased to **`0.9897`** from **`0.9678`**, even though the model is same. This is because the `final_lightgbm` variable is trained on the complete dataset including the test/hold-out set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgKSkSsZzZwv"
   },
   "source": [
    "# 13.0 Predict on Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6n7QFM94zZwy"
   },
   "source": [
    "The `predict_model()` function is also used to predict on the unseen dataset. The only difference from section 11 above is that this time we will pass the `data_unseen` parameter. `data_unseen` is the variable created at the beginning of the tutorial and contains 10% (600 samples) of the original dataset which was never exposed to PyCaret. (see section 5 for explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YdlpJUx0zZw4",
    "outputId": "5b45a2b5-c9a1-4d20-80f7-28211f07d586"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.53</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>12791</td>\n",
       "      <td>13726.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Fair</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10450</td>\n",
       "      <td>9832.7325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5161</td>\n",
       "      <td>5084.2239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.51</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>34361</td>\n",
       "      <td>34806.0152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4238</td>\n",
       "      <td>4070.4143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.53      Ideal     E     SI1     ID       ID   AGSL  12791   \n",
       "1          1.50       Fair     F     SI1     VG       VG    GIA  10450   \n",
       "2          1.01       Good     E     SI1      G        G    GIA   5161   \n",
       "3          2.51  Very Good     G     VS2     VG       VG    GIA  34361   \n",
       "4          1.01       Good     I     SI1     VG       VG    GIA   4238   \n",
       "\n",
       "        Label  \n",
       "0  13726.0144  \n",
       "1   9832.7325  \n",
       "2   5084.2239  \n",
       "3  34806.0152  \n",
       "4   4070.4143  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_lightgbm, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZnpuHoDzZxG"
   },
   "source": [
    "The `Label` column is added onto the `data_unseen` set. Label is the predicted value using the `final_lightgbm` model. If you want predictions to be rounded, you can use `round` parameter inside `predict_model()`. You can also check the metrics on this since you have actual target column `Price` available. To do that we will use pycaret.utils module. See example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9776"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import check_metric\n",
    "check_metric(unseen_predictions.Price, unseen_predictions.Label, 'R2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "os2dbiIrzZxJ"
   },
   "source": [
    "# 14.0 Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46CV19RlzZxL"
   },
   "source": [
    "We have now finished the experiment by finalizing the `tuned_lightgbm` model which is now stored in `final_lightgbm` variable. We have also used the model stored in `final_lightgbm` to predict `data_unseen`. This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tXl6hkG9zZxN",
    "outputId": "4af19b19-6c0c-4b1e-a4f7-249e729091bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(final_lightgbm,'Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2wgdZ5ozZxX"
   },
   "source": [
    "(TIP : It's always good to use date in the filename when saving models, it's good for version control.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LsyznpCzZxb"
   },
   "source": [
    "# 15.0 Loading the Saved Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZH-4EMLzZxd"
   },
   "source": [
    "To load a saved model at a future date in the same or an alternative environment, we would use PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2hsqdgn3zZxg",
    "outputId": "9db6b97b-ea93-452b-e4b4-121bea203bd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_final_lightgbm = load_model('Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NBAXt62nzZx5"
   },
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function. Below we have applied the loaded model to predict the same `data_unseen` that we used in section 13 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7debJpCzZx8"
   },
   "outputs": [],
   "source": [
    "new_prediction = predict_model(saved_final_lightgbm, data=data_unseen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8hT1v3N0zZyD",
    "outputId": "c5f9994b-1859-49d0-9bb0-4d7f6c506b98"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.53</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>12791</td>\n",
       "      <td>13726.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.50</td>\n",
       "      <td>Fair</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5161</td>\n",
       "      <td>5084.2239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.51</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>34361</td>\n",
       "      <td>34806.0152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4238</td>\n",
       "      <td>4070.4143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.53      Ideal     E     SI1     ID       ID   AGSL  12791   \n",
       "1          1.50       Fair     F     SI1     VG       VG    GIA  10450   \n",
       "2          1.01       Good     E     SI1      G        G    GIA   5161   \n",
       "3          2.51  Very Good     G     VS2     VG       VG    GIA  34361   \n",
       "4          1.01       Good     I     SI1     VG       VG    GIA   4238   \n",
       "\n",
       "        Label  \n",
       "0  13726.0144  \n",
       "1   9832.7325  \n",
       "2   5084.2239  \n",
       "3  34806.0152  \n",
       "4   4070.4143  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_prediction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cuVEPftKzZyK"
   },
   "source": [
    "Notice that the results of `unseen_predictions` and `new_prediction` are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9776"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import check_metric\n",
    "check_metric(new_prediction.Price, new_prediction.Label, 'R2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# 16.0 Wrap-up / Next Steps?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ct8oOjuvzZyS"
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   "source": [
    "This tutorial has covered the entire machine learning pipeline from data ingestion, pre-processing, training the model, hyperparameter tuning, prediction and saving the model for later use. We have completed all of these steps in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `tune_model()`, `compare_models()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most libraries.\n",
    "\n",
    "We have only covered the basics of `pycaret.regression`. In following tutorials we will go deeper into advanced pre-processing, ensembling, generalized stacking and other techniques that allow you to fully customize your machine learning pipeline and are must know for any data scientist.\n",
    "\n",
    "See you at the next tutorial. Follow the link to __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__"
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xHqY/gC3BpqZXgOh67efNfBF3SgqvtehJsj+I37KRtsSha2S4dDKSHGT36oSs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+W/+wMNqzYSWF9PzHaMm9e362u2Ri+lSneG8LqNPU9/TZNFD/6LI+/9WZev18/6tItBB4cZvG1l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F+Zz/6F3ofVAWv3D5jSx6+OmuYiLDpHbxp9wSHM1Vn73DWQ/+jjPvv52P7nuU1S/P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\ncJIIqlZ/jN16SUG03E1LgWMrZAdZqCkgZEjMSIR0j0GeR2K28sZ2HMVLD/6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        "metadata": {
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 576x396 with 2 Axes>"
       }
      ]
     }
    },
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     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "Processing: ",
      "description_tooltip": null,
      "layout": "IPY_MODEL_baf068abf86f44a9a9502f79dfe17eb5",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_14244d0fca4f40a8b3552675869748a3",
      "value": 4
     }
    },
    "5eb7949ac4a140118e9ee6be49921284": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
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      "_view_name": "ProgressView",
      "bar_style": "",
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